Natural turn measures predict recurrent falls in community-dwelling older adults: a longitudinal cohort study

Real-world gait and turning in individuals scheduled for total knee arthroplasty

BACKGROUND

Improving mobility - specifically walking - is an important treatment goal of total knee arthroplasty. Objective indicators for mobility, however, are lacking in clinical evaluations. This study aimed to compare real-world gait and turning between individuals scheduled for total knee arthroplasty and healthy controls, using continuous monitoring with inertial measurement units.

METHODS

Real-world gait and turning data were collected for 5-7 days in individuals scheduled for total knee arthroplasty (n = 34) and healthy controls (n = 32) using inertial measurement units on the feet and lower back. Gait and turning parameters were compared between groups using a linear regression model. Data was further analyzed by stratification of gait bouts based on bout length, and turns based on turning angle and turning direction.

FINDINGS

Dominant real-world gait speed was 0.21 m/s lower in individuals scheduled for total knee arthroplasty compared to healthy controls. Stride time was 0.05 s higher in individuals scheduled for total knee arthroplasty. Step time asymmetry was not different between the groups. Regarding walking activity, individuals scheduled for total knee arthroplasty walked 72 strides/h less than healthy controls, and maximum bout length was 316 strides shorter. Irrespective of the size of the turn, turning velocity was lower in individuals scheduled for total knee arthroplasty.

INTERPRETATION

Individuals scheduled for total knee arthroplasty showed specific walking and turning limitations in the real-world. Parameters derived from inertial measurement units reflected a rich profile of real-world mobility measures indicative of walking limitation of individuals scheduled for total knee arthroplasty, which may provide a relevant outcome dimension for future studies.

Mobility Requirements and Joint Loading during Straight Walking and 90° Turns in Healthy Older People and Those with Hip Osteoarthritis

Background/Objectives: Hip mobility and joint loading in hip osteoarthritis (HOA) patients are mostly assessed during straight walking. Yet, mobility limitations in the frontal and transverse planes are rarely found during this task in subjects with mild-to-moderate symptoms. Turning movements are frequently encountered during everyday life and might require larger hip mobility compared to straight walking, especially in the frontal and transverse planes. Thus, hip mobility and hip loading during straight walking and 90° turns in persons with HOA and healthy older adults were compared in this study. Methods: A retrospective analysis was conducted on 21 subjects with mild-to-moderate HOA and 21 healthy controls. Hip angles and moments were assessed during straight walking and 90° step and spin turns. Gait analysis was conducted using a motion capture system and a force plate. Group and movement task differences were assessed with a mixed-model ANOVA. Results: Peak abduction and adduction angles were largest during the step and spin turn, respectively, as were the group differences between HOA subjects and healthy subjects. Both turns require a greater transverse hip range of motion compared to straight walking. Limitations in transverse hip mobility in the HOA group were especially prominent during the step turn. Both turns cause higher joint moments than straight walking. Conclusions: The additional inclusion of 90° step and spin turns into gait analysis can enhance early identification of hip mobility limitations in the frontal and transverse planes in subjects with mild-to-moderate hip osteoarthritis. Early diagnosis is crucial for the timely application of conservative treatment strategies.

Walking on a Balance Beam as a New Measure of Dynamic Balance to Predict Falls in Older Adults and Patients with Neurological Conditions

BACKGROUND

Beam walking is a new test to estimate dynamic balance. We characterized dynamic balance measured by the distance walked on beams of different widths in five age groups of healthy adults (20, 30, 40, 50, 60 years) and individuals with neurological conditions (i.e., Parkinson, multiple sclerosis, stroke, age: 66.9 years) and determined if beam walking distance predicted prospective falls over 12 months.

METHODS

Individuals with (n = 97) and without neurological conditions (n = 99, healthy adults, age 20-60) participated in this prospective longitudinal study. Falls analyses over 12 months were conducted. The summed distance walked under single (walking only) and dual-task conditions (walking and serial subtraction by 7 between 300 to 900) on three beams (4, 8, and 12-cm wide) was used in the analyses. Additional functional tests comprised grip strength and the Short Physical Performance Battery.

RESULTS

Beam walking distance was unaffected on the 12-cm-wide beam in the healthy adult groups. The distance walked on the 8-cm-wide beam decreased by 0.34 m in the 20-year-old group. This reduction was ~ 3 × greater, 1.1 m, in the 60-year-old group. In patients, beam walking distances decreased sharply by 0.8 m on the 8 versus 12 cm beam and by additional 1.6 m on the 4 versus 8 cm beam. Beam walking distance under single and dual-task conditions was linearly but weakly associated with age (R2 = 0.21 for single task, R2 = 0.27 for dual-task). Age, disease, and beam width affected distance walked on the beam. Beam walking distance predicted future falls in the combined population of healthy adults and patients with neurological conditions. Based on receiver operating characteristic curve analyses using data from the entire study population, walking ~ 8.0 of the 12 m maximum on low-lying beams predicted future fallers with reasonable accuracy.

CONCLUSION

Balance beam walking is a new but worthwhile measure of dynamic balance to predict falls in the combined population of healthy adults and patients with neurological conditions. Future studies are needed to evaluate the predictive capability of beam walking separately in more homogenous populations. Clinical Trial Registration Number NCT03532984.

Digital gait measures, but not the 400-meter walk time, detect abnormal gait characteristics in people with Prediabetes

BACKGROUND AND AIM

Abnormal gait characteristics have been observed in people with diabetic neuropathy, but it is unclear if subtle changes in gait occur in prediabetic people with impaired fasting glucose (IFG). The aims of this study were: (1) to investigate if digital gait measures discriminate people with prediabetes from healthy control participants (HC) and (2) to investigate the relationship between gait measures and clinical scores (concurrent validity).

METHODS

108 people with prediabetes (71.20 ± 5.11 years) and 63 HC subjects (70.40 ± 6.25 years) wore 6 inertial sensors (Opals by APDM, Clario) while performing the 400-meter fast walk test. Fifty-five measures across 5 domains of gait (Lower Body, Upper Body, Turning, and Variability) were averaged. Analysis of Covariance was used to investigate the group differences, with body mass index as a covariate. Pearson's correlation coefficient assessed the association between the gait measures and the Short Physical Performance Battery (SPPB) score.

RESULTS

Nine gait measures were significantly different (p < 10-4) between IFG and HC groups. Step duration, cadence, and turn velocity were the most discriminative measures. In contrast, traditional stop-watch time was not significantly different between groups (p = 0.13), after controlling for BMI. Cadence (r = -0.37, p < 0.001), step duration (r = -0.39, p < 0.001), and turn velocity (r = 0.47, p < 0.001) showed a significant correlation with the SPPB score.

CONCLUSION

Body-worn inertial sensors detected gait impairments in people with prediabetes that related to clinical balance test performance, even when the traditional stop-watch time was not prolonged for the 400-meter walk test.

Turning speed as a more responsive metric of age-related decline in mobility: A comparative study with gait speed

BACKGROUND

Navigating your environment requires both straight-line gait as well as turning. Gait speed normative values are well established and utilized in determining a person's functional status, however, it has limitations. This study sought to examine whether turning speed declines with age and how it compared to gait speed age-related decline.

METHODS

A secondary analysis was performed on 275 community dwelling adults between the ages of 18-88 that performed a timed walking test with an inertial measurement unit on their lumbar spine. Turning speed and walking speed were extracted for each participant. A series of mixed models were compared, and Akaike's Information Criterion was used to determine the best fit model between age and turning speed and age and gait speed.

FINDINGS

Turning speed and gait speed normative values were reported for each age decade. A linear model with a random intercept of "Condition" was used to assess the relationship between age and turning speed. The results indicated a significant negative relationship between age and turning speed (B = -0.66, p < 0.001). A spline-fit model determined a significant negative relationship between age and gait speed after the age of 65 (B = -0.0097, p = 0.002). The effect of age on gait speed before age 65 was not significant.

INTERPRETATION

Turning speed significantly declines with age in a linear fashion while gait speed begins to decline after age 65. Turning speed may be more responsive to age than gait speed. More research is needed to determine if the decline in turning speed with age is associated with a decline in function.

Quantifying Turning Tasks With Wearable Sensors: A Reliability Assessment

OBJECTIVE

The aim of this study was to establish the test-retest reliability of metrics obtained from wearable inertial sensors that reflect turning performance during tasks designed to imitate various turns in daily activity.

METHODS

Seventy-one adults who were healthy completed 3 turning tasks: a 1-minute walk along a 6-m walkway, a modified Illinois Agility Test (mIAT), and a complex turning course (CTC). Peak axial turning and rotational velocity (yaw angular velocity) were extracted from wearable inertial sensors on the head, trunk, and lumbar spine. Intraclass correlation coefficients (ICCs) were established to assess the test-retest reliability of average peak turning speed for each task. Lap time was collected for reliability analysis as well.

RESULTS

Turning speed across all tasks demonstrated good to excellent reliability, with the highest reliability noted for the CTC (45-degree turns: ICC = 0.73-0.81; 90-degree turns: ICC = 0.71-0.83; and 135-degree turns: ICC = 0.72-0.80). The reliability of turning speed during 180-degree turns from the 1-minute walk was consistent across all body segments (ICC = 0.74-0.76). mIAT reliability ranged from fair to excellent (end turns: ICC = 0.52-0.72; mid turns: ICC = 0.50-0.56; and slalom turns: ICC = 0.66-0.84). The CTC average lap time demonstrated good test-retest reliability (ICC = 0.69), and the mIAT average lap time test-retest reliability was excellent (ICC = 0.91).

CONCLUSION

Turning speed measured by inertial sensors is a reliable outcome across a variety of ecologically valid turning tasks that can be easily tested in a clinical environment.

IMPACT

Turning performance is a reliable and important measure that should be included in clinical assessments and clinical trials.

A Scoping Review of the Validity and Reliability of Smartphone Accelerometers When Collecting Kinematic Gait Data

The aim of this scoping review is to evaluate and summarize the existing literature that considers the validity and/or reliability of smartphone accelerometer applications when compared to 'gold standard' kinematic data collection (for example, motion capture). An electronic keyword search was performed on three databases to identify appropriate research. This research was then examined for details of measures and methodology and general study characteristics to identify related themes. No restrictions were placed on the date of publication, type of smartphone, or participant demographics. In total, 21 papers were reviewed to synthesize themes and approaches used and to identify future research priorities. The validity and reliability of smartphone-based accelerometry data have been assessed against motion capture, pressure walkways, and IMUs as 'gold standard' technology and they have been found to be accurate and reliable. This suggests that smartphone accelerometers can provide a cheap and accurate alternative to gather kinematic data, which can be used in ecologically valid environments to potentially increase diversity in research participation. However, some studies suggest that body placement may affect the accuracy of the result, and that position data correlate better than actual acceleration values, which should be considered in any future implementation of smartphone technology. Future research comparing different capture frequencies and resulting noise, and different walking surfaces, would be useful.

A pilot study comparing prosthetic to sound limb gait mechanics during a turning task in people with transtibial amputation

BACKGROUND

Observational gait analysis is frequently used by clinicians to subjectively assess straight walking but is not often used to examine turning. Interlimb comparisons of phase- specific turning biomechanics in people with unilateral lower limb amputation has not previously been documented.

METHODS

A retrospective examination of gait kinematics and kinetics from five participants with unilateral transtibial amputation was performed. Data were collected during 90° step and spin turns capturing three distinct turning steps. Gait metrics of interest included: total turn time, stance time, peak knee flexion angle during Pre-Swing and Initial Swing gait phases, peak hip flexion and extension, ground reaction impulse, and whole body angular momentum. Statistical comparisons were made based on turn type between sound and prosthetic limbs.

FINDINGS

During the three turn steps (approach, apex, depart), participants spent significantly more time (P < 0.01) on their sound limb compared to their prosthetic limb regardless of turn type. Additionally, the prosthetic limb hip and knee exhibited more flexion (P < 0.05) during the apex step of turns, and whole body angular momentum was higher when the sound limb was used during the apex step of a turn (P < 0.05).

INTERPRETATION

This descriptive study offers the first phase-specific quantification of turning biomechanics in people with lower limb amputation. Results indicate that people with unilateral transtibial amputation spend more time on and experience higher impulses through their sound compared to their prosthetic limb during 90° turns, and that the prosthetic limb is performing differently than the sound limb, potentially increasing risks of injury or falls.

Machine learning based estimation of dynamic balance and gait adaptability in persons with neurological diseases using inertial sensors

Poor dynamic balance and impaired gait adaptation to different contexts are hallmarks of people with neurological disorders (PwND), leading to difficulties in daily life and increased fall risk. Frequent assessment of dynamic balance and gait adaptability is therefore essential for monitoring the evolution of these impairments and/or the long-term effects of rehabilitation. The modified dynamic gait index (mDGI) is a validated clinical test specifically devoted to evaluating gait facets in clinical settings under a physiotherapist's supervision. The need of a clinical environment, consequently, limits the number of assessments. Wearable sensors are increasingly used to measure balance and locomotion in real-world contexts and may permit an increase in monitoring frequency. This study aims to provide a preliminary test of this opportunity by using nested cross-validated machine learning regressors to predict the mDGI scores of 95 PwND via inertial signals collected from short steady-state walking bouts derived from the 6-minute walk test. Four different models were compared, one for each pathology (multiple sclerosis, Parkinson's disease, and stroke) and one for the pooled multipathological cohort. Model explanations were computed on the best-performing solution; the model trained on the multipathological cohort yielded a median (interquartile range) absolute test error of 3.58 (5.38) points. In total, 76% of the predictions were within the mDGI's minimal detectable change of 5 points. These results confirm that steady-state walking measurements provide information about dynamic balance and gait adaptability and can help clinicians identify important features to improve upon during rehabilitation. Future developments will include training of the method using short steady-state walking bouts in real-world settings, analysing the feasibility of this solution to intensify performance monitoring, providing prompt detection of worsening/improvements, and complementing clinical assessments.

The Instrumented Stand and Walk (ISAW) test to predict falls in older men

Objective measures of balance and gait have the potential to improve prediction of future fallers because balance and gait impairments are common precursors. We used the Instrumented Stand and Walk Test (ISAW) with wearable, inertial sensors to maximize the domains of balance and gait evaluated in a short test. We hypothesized that ISAW objective measures across a variety of gait and balance domains would improve fall prediction beyond history of falls and better than gait speed or dual-task cost on gait-speed. We recruited 214 high-functioning older men (mean 82 years), of whom 91 participants (42.5%) had one or more falls in the 12 months following the ISAW test. The ISAW test involved 30 s of stance followed by a 7-m walk, turn, and return. We examined regression models for falling using 17 ISAW metrics, with and without age and fall history, and characterize top-performing models by AUC and metrics included. The ISAW test improved distinguishing between future fallers and non-fallers compared to age and history of falls, alone (AUC improved from 0.69 to 0.75). Models with 1 ISAW metric usually included a postural sway measure, models with 2 ISAW measures included a turning measure, models with 3 ISAW measures included a gait variability measure, and models with 4 or 5 measures added a gait initiation measure. Gait speed and dual-task cost did not distinguish between fallers and non-fallers in this high-functioning cohort. The best fall-prediction models support the notion that older people may fall due to a variety of balance and gait impairments.

Gait and turning characteristics from daily life increase ability to predict future falls in people with Parkinson's disease

Objectives

To investigate if digital measures of gait (walking and turning) collected passively over a week of daily activities in people with Parkinson's disease (PD) increases the discriminative ability to predict future falls compared to fall history alone.

Methods

We recruited 34 individuals with PD (17 with history of falls and 17 non-fallers), age: 68 ± 6 years, MDS-UPDRS III ON: 31 ± 9. Participants were classified as fallers (at least one fall) or non-fallers based on self-reported falls in past 6 months. Eighty digital measures of gait were derived from 3 inertial sensors (Opal® V2 System) placed on the feet and lower back for a week of passive gait monitoring. Logistic regression employing a "best subsets selection strategy" was used to find combinations of measures that discriminated future fallers from non-fallers, and the Area Under Curve (AUC). Participants were followed via email every 2 weeks over the year after the study for self-reported falls.

Results

Twenty-five subjects reported falls in the follow-up year. Quantity of gait and turning measures (e.g., number of gait bouts and turns per hour) were similar in future fallers and non-fallers. The AUC to discriminate future fallers from non-fallers using fall history alone was 0.77 (95% CI: [0.50-1.00]). In contrast, the highest AUC for gait and turning digital measures with 4 combinations was 0.94 [0.84-1.00]. From the top 10 models (all AUCs>0.90) via the best subsets strategy, the most consistently selected measures were variability of toe-out angle of the foot (9 out of 10), pitch angle of the foot during mid-swing (8 out of 10), and peak turn velocity (7 out of 10).

Conclusions

These findings highlight the importance of considering precise digital measures, captured via sensors strategically placed on the feet and low back, to quantify several different aspects of gait (walking and turning) during daily life to improve the classification of future fallers in PD.

Effect of Lower Limb vs. Abdominal Compression on Mobility in Orthostatic Hypotension: A Single-Blinded, Randomized, Controlled, Cross-Over Pilot Study in Parkinson's Disease

BACKGROUND

Orthostatic hypotension (OH) in Parkinson's disease (PD) is frequent and associated with impairments in quality of life and reduced activities of daily living. Abdominal binders (AB) and compression stockings (CS) have been shown to be effective non-pharmacological treatment options.

OBJECTIVE

Here, we investigate the effect of AB versus CS on physical activity using a digital mobility outcome (sit to stand [STS] frequency) collected in the usual environment as a primary endpoint.

METHODS

We enrolled 16 PD patients with at least moderate symptomatic OH. In a randomized, single-blinded, controlled, crossover design, participants were assessed without OH treatment over 1 week (baseline), then were given AB or CS for 1 week and subsequently switched to the other treatment arm. The primary outcome was the number of real-life STS movements per hour as assessed with a lower back sensor. Secondary outcomes included real-life STS duration, mean/systolic/diastolic blood pressure drop (BPD), orthostatic hypotension questionnaire (OHQ), PD quality of life (PDQ-39), autonomic symptoms (SCOPA-AUT), non-motor symptoms (NMSS), MDS-UPDRS, and activities of daily living (ADL/iADL).

RESULTS

Real-life STS frequency on CS was 4.4±4.1 per hour compared with 3.6±2.2 on AB and 3.6±1.8 without treatment (p = 1.0). Concerning the secondary outcomes, NMSS showed significant improvement with CS and AB. OHQ and SCOPA-AUT improved significantly with AB but not CS, and mean BPD drop worsened with CS but not AB. Mean STS duration, PDQ-39, MDS-UPDRS, ADL, and iADL did not significantly change.

CONCLUSION

Both AB and CS therapies do not lead to a significant change of physical activity in PD patients with at least moderate symptomatic OH. Secondary results speak for an effect of both therapies concerning non-motor symptoms, with superiority of AB therapy over CS therapy.

Using Wearable Inertial Sensors to Assess Mobility of Patients With Hematologic Cancer and Associations With Chemotherapy-Related Symptoms Before Autologous Hematopoietic Stem Cell Transplant: Cross-sectional Study

BACKGROUND

Wearable sensors could be a simple way to quantify and characterize mobility in patients with hematologic cancer scheduled to receive autologous hematopoietic stem cell transplant (autoHSCT) and how they may be related to common treatment-related symptoms and side effects of induction chemotherapy.

OBJECTIVE

We aimed to conduct a cross-sectional study comparing mobility in patients scheduled to receive autoHSCT with that in healthy, age-matched adult controls and determine the relationships between patient mobility and chemotherapy-related symptoms.

METHODS

Patients scheduled to receive autoHSCT (78/156, 50%) and controls (78/156, 50%) completed the prescribed performance tests using wearable inertial sensors to quantify mobility including turning (turn duration and number of steps), gait (gait speed, stride time, stride time variability, double support time, coronal trunk range of motion, heel strike angle, and distance traveled), and balance (coronal sway, coronal range, coronal velocity, coronal centroidal frequency, sagittal sway, sagittal range, sagittal velocity, and sagittal centroidal frequency). Patients completed the validated patient-reported questionnaires to assess symptoms common to chemotherapy: chemotherapy-induced peripheral neuropathy (Functional Assessment of Cancer Therapy/Gynecologic Oncology Group-Neurotoxicity subscale), nausea and pain (European Organization for Research and Treatment of Cancer Quality of Life Questionnaire), fatigue (Patient-Reported Outcomes Measurement Information System Fatigue Short Form 8a), vertigo (Vertigo Symptom Scale-short form), and depression (Center for Epidemiological Studies-Depression). Paired, 2-sided t tests were used to compare mobility between patients and controls. Stepwise multivariable linear regression models were used to evaluate associations between patient mobility and symptoms.

RESULTS

Patients aged 60.3 (SD 10.3) years had significantly worse turning (turn duration; P<.001), gait (gait speed, stride time, stride time variability, double support time, heel strike angle, stride length, and distance traveled; all P<.001), and balance (coronal sway; P<.001, range; P<.001, velocity; P=.02, and frequency; P=.02; and sagittal range; P=.008) than controls. In patients, high nausea was associated with worse stride time variability (ß=.001; P=.005) and heel strike angle (ß=-.088; P=.02). Pain was associated with worse gait speed (ß=-.003; P=.003), stride time variability (ß=.012; P=.02), stride length (ß=-.002; P=.004), and distance traveled (ß=-.786; P=.005). Nausea and pain explained 17% to 33% and 14% to 36% of gait variance measured in patients, respectively.

CONCLUSIONS

Patients scheduled to receive autoHSCT demonstrated worse mobility in multiple turning, gait, and balance domains compared with controls, potentially related in part to nausea and pain. Wearable inertial sensors used in the clinic setting could provide granular information about mobility before further treatment, which may in turn benefit from rehabilitation or symptom management. Future longitudinal studies are needed to better understand temporal changes in mobility and symptoms across the treatment trajectory to optimally time, design, and implement strategies, to preserve functioning in patients with hematologic cancer in the long term.

Developing Acute Event Risk Profiles for Older Adults with Dementia in Long-Term Care Using Motor Behavior Clusters Derived from Deep Learning

OBJECTIVES

This paper uses deep (machine) learning techniques to develop and test how motor behaviors, derived from location and movement sensor tracking data, may be associated with falls, delirium, and urinary tract infections (UTIs) in long-term care (LTC) residents.

DESIGN

Longitudinal observational study.

SETTING AND PARTICIPANTS

A total of 23 LTC residents (81,323 observations) with cognitive impairment or dementia in 2 northeast Department of Veterans Affairs LTC facilities.

METHODS

More than 18 months of continuous (24/7) monitoring of motor behavior and activity levels used objective radiofrequency identification sensor data to track and record movement data. Occurrence of acute events was recorded each week. Unsupervised deep learning models were used to classify motor behaviors into 5 clusters; supervised decision tree algorithms used these clusters to predict acute health events (falls, delirium, and UTIs) the week before the week of the event.

RESULTS

Motor behaviors were classified into 5 categories (Silhouette score = 0.67), and these were significantly different from each other. Motor behavior classifications were sensitive and specific to falls, delirium, and UTI predictions 1 week before the week of the event (sensitivity range = 0.88-0.91; specificity range = 0.71-0.88).

CONCLUSION AND IMPLICATIONS

Intraindividual changes in motor behaviors predict some of the most common and detrimental acute events in LTC populations. Study findings suggest real-time locating system sensor data and machine learning techniques may be used in clinical applications to effectively prevent falls and lead to the earlier recognition of risk for delirium and UTIs in this vulnerable population.

TURN-IT: a novel turning intervention program to improve quality of turning in daily life in people with Parkinson's disease

BACKGROUND

People with Parkinson's disease (PD) have a high fall rate and many falls are associated with turns. Despite this, there is minimal research on effects of rehabilitation on the quality of turns. Further, quantifying turns in the home may have broader implications since rehabilitation of turns would ideally improve turning in real world mobility.

METHODS

Sixty people with PD and a history of falls will be randomized to receive either a novel TURNing InTervention (TURN-IT) or no intervention (control group). The TURN-IT group will be seen for 6 weeks (18 visits) for an individualized, progressive program that is based on the specific constraints of turning in PD. Wearable sensors will be used to measure 7 days of mobility, including turns, before and after intervention or control period. In addition, blinded assessments of gait, mobility and turns will occur before and after intervention for both groups and falls will be monitored for twelve months post intervention with bimonthly email questionnaires.

DISCUSSION

This study has the potential to change how we rehabilitate and assess turning in people with PD and falls. There are several novel aspects to our study including a comprehensive turning-focused intervention that is tailored to the underlying constraints that impair turning in people with PD. Further, our outcome measure of turning quality during 7 days of daily life is novel and has implications for determining real-life changes after rehabilitation. The ultimate goal of this rehabilitation intervention is to improve how patients turn in daily life and to reduce falls.

TRIALS REGISTRATION

This protocol is registered at clinicaltrials.gov; #NCT04897256; https://clinicaltrials.gov/ct2/show/NCT04897256?term=Horak&cond=Parkinson+Disease&draw=2&rank=4 .

Digitizing a Therapeutic: Development of an Augmented Reality Dual-Task Training Platform for Parkinson's Disease

Augmented reality (AR) may be a useful tool for the delivery of dual-task training. This manuscript details the development of the Dual-task Augmented Reality Treatment (DART) platform for individuals with Parkinson's disease (PD) and reports initial feasibility, usability, and efficacy of the DART platform in provoking dual-task interference in individuals with PD. The DART platform utilizes the head-mounted Microsoft HoloLens2 AR device to deliver concurrent motor and cognitive tasks. Biomechanical metrics of gait and cognitive responses are automatically computed and provided to the supervising clinician. To assess feasibility, individuals with PD (N = 48) completed a bout of single-task and dual-task walking using the DART platform. Usability was assessed by the System Usability Scale (SUS). Dual-task interference was assessed by comparing single-task walking and walking during an obstacle course while performing a cognitive task. Average gait velocity decreased from 1.06 to 0.82 m/s from single- to dual-task conditions. Mean SUS scores were 81.3 (11.3), which placed the DART in the "good" to "excellent" category. To our knowledge, the DART platform is the first to use a head-mounted AR system to deliver a dual-task paradigm and simultaneously provide biomechanical data that characterize cognitive and motor performance. Individuals with PD were able to successfully use the DART platform with satisfaction, and dual-task interference was provoked. The DART platform should be investigated as a platform to treat dual-task declines associated with PD.

Agreement and participants’ preferences comparing: self-rated falls risk questionnaire (FRQ) and activities-specific balance confidence (ABC) scale in community-dwelling older adults using the Bland–Altman method

Purpose

Screening for fall risks is an important part of fall and fracture prevention. This study aims to investigate cross-sectional inter-instrumental agreement and participants’ preferences of the self-rated Falls Risk Questionnaire (FRQ) and Activities Specific Balance Confidence 6 items (ABC-6). This study also aimed to compare FRQ and ABC-6 scores in older adults with and without a history of falls.

Design/methodology/approach

Through an online and snowball sampling survey, 114 respondents were recruited from six countries. Respondents were asked to perform FRQ and ABC-6 surveys.

Findings

The mean respondent age was 67 years, and 44.8% reported falls in the past year. The mean of rescored FRQ and ABC-6 scores were 68.6% and 66.2%, respectively. The FRQ and ABC-6 scores for fallers were lower than non-fallers. Bland and Altman’s method indicated the mean −2.6 and two standard deviations 20.9 differences between ABC-6 and FRQ, which means an overall agreement between these tools. Most of the respondents, 36% had no preference between ABC-6 and FRQ, 34% preferred none, 21% preferred the ABC-6 and 9% preferred the FRQ for screening future falls risk.

Originality/value

Both ABC-6 and FRQ can distinguish between fallers and non-fallers, and findings of this study can be used to support the use of the FRQ for falls screening in older adults.

Wearable technology in orthopedic trauma surgery - An AO trauma survey and review of current and future applications

The use of wearable sensors to track activity is increasing. Therefore, a survey among AO Trauma members was conducted to provide an overview of their current utilization and determine future needs and directions. A cross sectional expert opinion survey was administered to members of AO Trauma. Respondents were surveyed concerning their experience, subspeciality, current use characteristics, as well as future needs concerning wearable technology. Three hundred and thirty-three survey sets were available for analysis (Response Rate 16.2%). 20.7% of respondents already use wearable technology as part of their clinical treatment. The most prevalent technology was accelerometry combined with smartphones (75.4%) to measure general patient activity. To facilitate the use of wearable technology in the future, the most pressing issues were cost, patient compliance and validity of results. Wearable activity monitors are currently being used in trauma surgery. Surgeons employing these technologies mostly measure simple activity or activity associated parameters. Cost was the greatest perceived barrier to implementation. Further research, especially concerning the interpretation of the outcome values obtained, is required to facilitate wearable activity monitoring as an objective patient outcome measurement tool.

High Specificity of Single Inertial Sensor-Supplemented Timed Up and Go Test for Assessing Fall Risk in Elderly Nursing Home Residents

The Timed Up and Go test (TUG) is commonly used to estimate the fall risk in the elderly. Several ways to improve the predictive accuracy of TUG (cameras, multiple sensors, other clinical tests) have already been proposed. Here, we added a single wearable inertial measurement unit (IMU) to capture the residents’ body center-of-mass kinematics in view of improving TUG’s predictive accuracy. The aim is to find out which kinematic variables and residents’ characteristics are relevant for distinguishing faller from non-faller patients. Data were collected in 73 nursing home residents with the IMU placed on the lower back. Acceleration and angular velocity time series were analyzed during different subtasks of the TUG. Multiple logistic regressions showed that total time required, maximum angular velocity at the first half-turn, gender, and use of a walking aid were the parameters leading to the best predictive abilities of fall risk. The predictive accuracy of the proposed new test, called i + TUG, reached a value of 74.0%, with a specificity of 95.9% and a sensitivity of 29.2%. By adding a single wearable IMU to TUG, an accurate and highly specific test is therefore obtained. This method is quick, easy to perform and inexpensive. We recommend to integrate it into daily clinical practice in nursing homes.

The Microsoft HoloLens 2 Provides Accurate Measures of Gait, Turning, and Functional Mobility in Healthy Adults

Augmented-reality (AR) headsets, such as the Microsoft HoloLens 2 (HL2), have the potential to be the next generation of wearable technology as they provide interactive digital stimuli in the context of ecologically-valid daily activities while containing inertial measurement units (IMUs) to objectively quantify the movements of the user. A necessary precursor to the widespread utilization of the HL2 in the fields of movement science and rehabilitation is the rigorous validation of its capacity to generate biomechanical outcomes comparable to gold standard outcomes. This project sought to determine equivalency of kinematic outcomes characterizing lower-extremity function derived from the HL2 and three-dimensional (3D) motion capture systems (MoCap). Sixty-six healthy adults completed two lower-extremity tasks while kinematic data were collected from the HL2 and MoCap: (1) continuous walking and (2) timed up-and-go (TUG). For all the continuous walking metrics (cumulative distance, time, number of steps, step and stride length, and velocity), equivalence testing indicated that the HL2 and MoCap were statistically equivalent (error ≤ 5%). The TUG metrics, including turn duration and turn velocity, were also statistically equivalent between the two systems. The accurate quantification of gait and turning using a wearable such as the HL2 provides initial evidence for its use as a platform for the development and delivery of gait and mobility assessments, including the in-person and remote delivery of highly salient digital movement assessments and rehabilitation protocols.

Differences in walking-to-turning characteristics between older adult fallers and nonfallers: a prospective and observational study using wearable inertial sensors

Wearable inertial sensors have gradually been used as an objective technology for biomechanical assessments of both healthy and pathological movement patterns. This paper used foot-worn sensors for characterizing the spatiotemporal characteristics of walking and turning between older fallers and nonfallers. Thirty community-dwelling older fallers and 30 older nonfallers performed 10-m straight walking, turned 180° around a cone, and then walked 10-m back to the starting point. Specific algorithms were used to measure spatiotemporal gait (double support phase of the gait cycle, swing width, and minimal toe clearance) and turning parameters (turn duration and turn steps) using two foot-worn Physiolog inertial sensor system. The researchers directly exported data as reported by the system. Our findings indicated that older fallers showed 26.58% longer time (P = 0.036) and 13.21% more steps (P = 0.038) compared to nonfallers during turning. However, both groups decreased their walking velocity (both P < 0.001), increased double support (both P = 0.001), and increased the swing width (both P = 0.001) during the transition from walking to turning. The older nonfallers additionally increased toe clearance (P = 0.001). Compared with the fallers, the older nonfallers showed a larger change in the swing width (P = 0.025) and toe clearance (P = 0.025) in walking to turning. Older fallers may adopt a cautionary strategy while turning to reduce the risk of falls. Wearable sensors can provide the temporospatial characteristics of turning and reveal significant differences by fall status, indicating the potential of turning measures as possible markers for identifying those at fall risk.

Pathway of Trends and Technologies in Fall Detection: A Systematic Review

Falling is one of the most serious health risk problems throughout the world for elderly people. Considerable expenses are allocated for the treatment of after-fall injuries and emergency services after a fall. Fall risks and their effects would be substantially reduced if a fall is predicted or detected accurately on time and prevented by providing timely help. Various methods have been proposed to prevent or predict falls in elderly people. This paper systematically reviews all the publications, projects, and patents around the world in the field of fall prediction, fall detection, and fall prevention. The related works are categorized based on the methodology which they used, their types, and their achievements.

The 180° Turn Phase of the Timed Up and Go Test Better Predicts History of Falls in the Oldest-Old When Compared With the Full Test: A Case-Control Study

The 180° turn phase of the test may better differentiate the oldest-old regarding their history of falls. This is a case-control study designed to detect the ability of the 180° turn timed up and go (TUG) phase to detect a history of falls in the oldest-old. Sixty people aged 85 years and older were assessed in their homes. The single-task and dual-task TUG tests were performed using an inertial sensor (G-Walk). Sociodemographic data, physical activity levels, mental status, depressive symptoms, concern for falls occurrence, number of medicines in use, self-perception of balance, and the functional reach test were also assessed. The logistic regressions revealed the 180° turn phase of both the single-task and dual-task TUG was almost three times better than the full TUG test to detect a history of falls, thus providing insights that can be used to better assess functional mobility in the oldest-old.

Between-site equivalence of turning speed assessments using inertial measurement units

BACKGROUND

Turning is a component of gait that requires planning for movement of multiple body segments and the sophisticated integration of sensory information from the vestibular, visual, and somatosensory systems. These aspects of turning have led to growing interest to quantify turning in clinical populations to characterize deficits or identify disease progression. However, turning may be affected by environmental differences, and the degree to which turning assessments are comparable across research or clinical sites has not yet been evaluated.

RESEARCH QUESTION

The aim of this study was to determine the extent to which peak turning speeds are equivalent between two sites for a variety of mobility tasks.

METHODS

Data were collected at two different sites using separate healthy young adult participants (n = 47 participants total), but recruited using identical inclusion and exclusion criteria. Participants at each site completed three turning tasks: a one-minute walk (1 MW) along a six-meter walkway, a modified Illinois Agility Test (mIAT), and a custom clinical turning course (CCTC). Peak yaw turning speeds were extracted from wearable inertial sensors on the head, trunk, and pelvis. Between-site differences and two one-sided tests (TOST) were used to determine equivalence between sites, based on a minimum effect size reported between individuals with mild traumatic brain injury and healthy control subjects.

RESULTS

No outcomes were different between sites, and equivalence was determined for 6/21 of the outcomes. These findings suggest that some turning tasks and outcome measures may be better suited for multi-site studies. The equivalence results are also dependent on the minimum effect size of interest; nearly all outcomes were equivalent across sites when larger minimum effect sizes of interest were used.

SIGNIFICANCE

Together, these results suggest some tasks and outcome measures may be better suited for multi-site studies and literature-based comparisons.

Instrumented Gait Analysis to Identify Persistent Deficits in Gait Stability in Adults With Chronic Vestibular Loss

Importance

Regaining the ability to walk safely is a high priority for adults with vestibular loss. Thus, practitioners need comprehensive knowledge of vestibulopathic gait to design, provide, and/or interpret outcomes of interventions. To date, few studies have characterized the effects of vestibular loss on gait.

Objectives

To investigate the use of an instrumented 2-minute walk test in adults with vestibular loss, to further characterize vestibulopathic gait, and to assess whether those with chronic vestibular loss have enduring gait deficits.

Design, Setting, and Participants

This cross-sectional study, conducted between April 3, 2018, and June 27, 2019, recruited adults 20 to 79 years of age from an academic, tertiary, hospital-based, ambulatory care setting who were healthy or had confirmed unilateral or bilateral vestibular hypofunction. Of the 43 adults who were screened from convenience and referred samples, 2 declined, and 7 were excluded because of health conditions.

Exposures

The main exposure was the instrumented 2-minute walk test, which was conducted with participants using wearable inertial measurement units while they walked a 10-m path at their self-selected speed and turned 180° in their self-selected direction at either end.

Main Outcomes and Measures

The primary measures were spatiotemporal gait metrics (eg, stride length [SL] and peak whole-body turning velocity). Multivariate analysis of variance was used to assess between-group differences. Validity was assessed using the area under the curve from receiver operator characteristic analyses.

Results

Data from 17 healthy adults (mean [SD] age, 39.27 [11.20] years; 13 [76%] female) and 13 adults with vestibular loss (mean [SD] age, 60.50 [10.81] years; 6 [46%] female) were analyzed. Very large between-group differences were found for SL (left) (estimated marginal mean [SE] for healthy vs vestibular groups, 1.47 [0.04] m vs 1.31 [0.04] m; Cohen d, 1.35; 95% CI, 0.18-2.52), SL (right) (estimated marginal mean [SE] for healthy vs vestibular groups, 1.46 [0.04] m vs 1.29 [0.04] m; Cohen d, 1.44; 95% CI, 0.25-2.62), and peak turn velocity (estimated marginal mean [SE] for healthy vs vestibular groups, 240.17 [12.78]°/s vs 189.74 [14.70]°/s; Cohen d, 1.23; 95% CI, 0.07-2.40). The area under the curve was 0.79 (95% CI, 0.62-0.95) for SL (left), 0.81 (95% CI, 0.64-0.97) for SL (right), and 0.86 (95% CI, 0.72-0.99) for peak turn velocity.

Conclusions and Relevance

In this cross-sectional study, instrumented gait analysis had good discriminative validity and revealed persistent deficits in gait stability in those with chronic vestibular loss. The findings of this study suggest that these clinically and functionally meaningful deficits could be targets for vestibular rehabilitation.

Basic Spatiotemporal Gait Variables of Young and Older Healthy Volunteers Walking Along a Novel Figure-of-8 Path

Background: Locomotion along curved trajectories requires fine coordination among body segments. Elderly people may adopt a cautious attitude when steering. A simple, expeditious, patient-friendly walking protocol can be a tool to help clinicians. We evaluated the feasibility of a procedure based upon a newly designed Figure-of-eight (nFo8) path and an easy measurement operation. Methods: Sixty healthy volunteers, aged from 20 to 86 years, walked three times at self-selected speed along a 20 m linear (LIN) and the 20 m nFo8 path. Number of steps, mean speed and walk ratio (step length/cadence) were collected. Data were analysed for the entire cohort and for the groups aged 20-45, 46-65, and >65 years. Results: There was no difference in mean LIN walking speed between the two younger groups but the oldest was slower. During nFo8, all groups were slower (about 16%) than during LIN. Cadence was not different across groups but lower during nFo8 in each group. Step length was about 8% shorter in the two younger groups and 14% shorter in the oldest during nFo8 compared to LIN. Walk ratio was the smallest in the oldest group for both LIN and nFo8. Conclusions: A complex nFo8 walking path, with fast and easy measurement of a simple set of variables, detects significant differences with moderate and large effects in gait variables in people >65 years. This challenging trajectory is more revealing than LIN. Further studies are needed to develop a quick clinical tool for assessment of gait conditions or outcome of rehabilitative treatments.

Turning difficulties after stroke and its relationship with trunk function

BACKGROUND

Turning difficulties has been reported in stroke patients, but most studies have indicated no differences in turning direction regarding turn time or steps. Recent evidence shows that turning difficulty may correlate with trunk control. Trunk flexibility and strength are considered essential to trunk control, but their association with turning performance has not been elucidated.

AIM

The study investigated the differences in turning direction in terms of turn duration and angular velocity and the relationship between turning performance and trunk function in patients with chronic stroke.

DESIGN

Cross-sectional study.

SETTING

Outpatient clinic at the Department of Physical Medicine and Rehabilitation.

POPULATION

Chronic stroke patients.

METHODS

Twenty-eight stroke patients were evaluated for turning performance and trunk function. Turn duration and angular velocity were assessed using three wearable sensors during 360° turning in place towards both sides. Trunk function, such as flexibility, strength, and control was measured using a tape measure, a microFET3 dynamometer, and the Trunk Impairment Scale.

RESULTS

Stroke patients showed significantly longer turn durations (4.62 ± 2.08 vs 3.59 ± 1.93 s, p = 0.036) and lower angular velocity (118.67 ± 35.78 vs 135.26 ± 42.41 0/s, p = 0.009) during turning toward the paretic side than towards the nonparetic side. The turning parameters towards the paretic side associated with trunk flexion (r = -0.550, p = 0.003) and rotation (r = 0.409, p = 0.034), trunk flexor strength (r = -0.387, p = 0.046), dynamic sitting balance (r = -0.383, p = 0.049) and coordination of trunk movement (r = -0.494, p = 0.009). However, no relationship was observed between trunk function and turning towards the nonparetic side.

CONCLUSIONS

Stroke participants experienced greater difficulty turning towards the paretic side. Trunk flexibility, strength, and control may affect turning performance, especially when turning towards the paretic side, which could explain the occurrence of falls after a turn towards the paretic side.

CLINICAL REHABILITATION IMPACT

Stroke patients experience turning difficulties, particularly during turning towards the paretic side. Stroke patients with limited trunk function are more likely to experience turning dysfunction. Clinical therapists should develop effective strategies for enhancing turning ability through improvement of trunk flexibility, strength, and control for clinical rehabilitation practice.

Turning assessment for discrimination of frailty syndrome among community-dwelling older adults

BACKGROUND

Frailty is a common geriatric syndrome and is characterized by decreased physiological reserve and increased vulnerability towards adverse health outcomes including falls. Turning is a challenging task and is reported to be one of the daily activities that leads to falling in older populations.

RESEARCH QUESTION

Does 180° walking turns and 360° turning on the spot differ among frail, pre-frail, and non-frail older adults? Can 180° walking turns and 360° turning on the spot cutoffs discriminate older adults with frailty from those without?

METHODS

A cross-sectional study was conducted on community-dwelling older adults aged over 65 years. Frailty was assessed using Fried's phenotype method, and turning tasks were measured by inertial sensors. The turn duration (s) and angular velocity (°/s) were recorded for analysis.

RESULTS

In total, 109 participants were enrolled including 50 pre-frail and 12 frail individuals. Frail older adults took significantly longer and had slower angular velocities to complete the 180° and 360° turning than did either pre-frail (p = 0.002 and p < 0.001, respectively) or non-frail (p = 0.03 and p < 0.001, respectively) older adults. Cutoff times of 2.45 and 3.46 s were found to best discriminate frail people from those without frailty in both the 180° (sensitivity 83.3 %, specificity 71.1 %, area under the receiver operating characteristic curve (AUC) 0.796) and 360° (sensitivity 91.7 %, specificity 74.2 %, AUC 0.857) turn tasks.

SIGNIFICANCE

Older individuals with frailty syndrome had difficulty turning as evidenced by a longer turning duration and a slower angular velocity. The turn duration could be a potential biomarker of frailty in older populations. Assessing the turning performance can facilitate early detection of the onset of frailty and inform early prevention and rehabilitation interventions in clinical practice.

Comparison of 360° Turn Cycles among Individuals after Stroke and Healthy Older Adults

Stroke survivors are at high risk of falling during turning. The kinematics of performing a 360° turn have not been fully analyzed among individuals after stroke. Quantitative differences in the parameters of turning between healthy older adults and those after stroke could provide detailed information on turning ability among these groups. The purpose of the current study was to characterize differences between healthy older adults and adults after stroke in 360° turn kinematics. Fourteen individuals with chronic stroke (mean age: 69 ± 8.4 years) and 14 healthy older adults (mean age: 74 ± 8.7 years) performed three trials of 360° turning. Kinematics data were collected using 26 reflective markers at several body landmarks. This new method for quantifying turning revealed that stroke significantly affected the number of turn cycles, number of single support (SS) critical phases, and critical time. In some cases, falls among individuals with stroke may be related to the combination of impaired movement patterns and the complexity of tasks such as turning. Understanding turning kinematics can inform clinical interventions targeting improvements in turning ability and consequently, fall risk reduction in individuals after stroke.

Middle-age people with multiple sclerosis demonstrate similar mobility characteristics to neurotypical older adults

BACKGROUND

Clinical trials often report significant mobility differences between neurotypical and atypical groups, however, these analyses often do not determine which measures are capable of discriminating between groups. Additionally, indirect evidence supports the notion that some mobility impaired populations demonstrate similar mobility deficits. Thus, the current study aimed to provide a comprehensive analysis of three distinct aspects of mobility (walking, turning, and balance) to determine which variables were significantly different and were also able to discriminate between neurotypical older adults (OA) and middle-aged people with multiple sclerosis (PwMS), and between middle-aged neurotypical adults and PwMS.

METHODS

This study recruited 21 neurotypical OA, 19 middle-aged neurotypical adults, and 30 people with relapsing remitting MS. Participants came into the laboratory on two separate occasions to complete mobility testing while wearing wireless inertial sensors. Testing included a self-selected pace two-minute walk, a series of 180˚ and 360˚ turns, and a clinical balance test capturing a total of 99 distinct mobility characteristics. We determined significant differences for gait and turning measures through univariate analyses and a series of repeated measures analysis of variance in determining significance for balance conditions and measures. In determining discrimination between groups, the Area Under the Curve (AUC) was calculated for all individual mobility measures with a threshold of 0.80, denoting excellent discrimination. Additionally, a stepwise regression of the top five AUC producing variables was performed to determine whether a combination of variables could enhance discrimination while accounting for multicollinearity.

RESULTS

The results between neurotypical OA and middle-aged PwMS demonstrated significant differences for three gait and one turning variable, with no variable or combination of variables able to provide excellent discrimination between groups. Between middle-age neurotypical adults and PwMS a variety of mean and variability gait measures demonstrated significant differences between groups; however, no variable or combination of variables met discriminatory threshold. For turning, five 360˚ turn variables demonstrated significant differences and furthermore, the combination of 360˚ mean turn duration and variability of peak turn velocity were able to discriminate between groups. Finally, the majority of postural sway measures demonstrated significant group differences and the ability to discriminate between groups, particularly during more challenging balance conditions where participants stood on a compliant surface.

CONCLUSION

These results offer a comprehensive analysis of mobility differences and measures capable of discriminating between middle-age neurotypical adults and PwMS. Additionally, these results provide evidence that OA and middle-age PwMS display similar movement characteristics and thus a potential indicator of advanced aging from a mobility perspective.

Independent and sensitive gait parameters for objective evaluation in knee and hip osteoarthritis using wearable sensors

Background

Although it is well-established that osteoarthritis (OA) impairs daily-life gait, objective gait assessments are not part of routine clinical evaluation. Wearable inertial sensors provide an easily accessible and fast way to routinely evaluate gait quality in clinical settings. However, during these assessments, more complex and meaningful aspects of daily-life gait, including turning, dual-task performance, and upper body motion, are often overlooked. The aim of this study was therefore to investigate turning, dual-task performance, and upper body motion in individuals with knee or hip OA in addition to more commonly assessed spatiotemporal gait parameters using wearable sensors.

Methods

Gait was compared between individuals with unilateral knee (n = 25) or hip OA (n = 26) scheduled for joint replacement, and healthy controls (n = 27). For 2 min, participants walked back and forth along a 6-m trajectory making 180° turns, with and without a secondary cognitive task. Gait parameters were collected using 4 inertial measurement units on the feet and trunk. To test if dual-task gait, turning, and upper body motion had added value above spatiotemporal parameters, a factor analysis was conducted. Effect sizes were computed as standardized mean difference between OA groups and healthy controls to identify parameters from these gait domains that were sensitive to knee or hip OA.

Results

Four independent domains of gait were obtained: speed-spatial, speed-temporal, dual-task cost, and upper body motion. Turning parameters constituted a gait domain together with cadence. From the domains that were obtained, stride length (speed-spatial) and cadence (speed-temporal) had the strongest effect sizes for both knee and hip OA. Upper body motion (lumbar sagittal range of motion), showed a strong effect size when comparing hip OA with healthy controls. Parameters reflecting dual-task cost were not sensitive to knee or hip OA.

Conclusions

Besides more commonly reported spatiotemporal parameters, only upper body motion provided non-redundant and sensitive parameters representing gait adaptations in individuals with hip OA. Turning parameters were sensitive to knee and hip OA, but were not independent from speed-related gait parameters. Dual-task parameters had limited additional value for evaluating gait in knee and hip OA, although dual-task cost constituted a separate gait domain. Future steps should include testing responsiveness of these gait domains to interventions aiming to improve mobility.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12891-021-04074-2.

Fall risk assessment in the wild: A critical examination of wearable sensor use in free-living conditions

BACKGROUND

Despite advances in laboratory-based supervised fall risk assessment methods (FRAs), falls still remain a major public health problem. This can be due to the alteration of behavior in laboratory due to the awareness of being observed (i.e., Hawthorne effect), the multifactorial complex etiology of falls, and our limited understanding of human behaviour in natural environments, or in the' wild'. To address these imitations, a growing body of literature has focused on free-living wearable-sensor-based FRAs. The objective of this narrative literature review is to discuss papers investigating natural data collected by wearable sensors for a duration of at least 24 h to identify fall-prone older adults.

METHODS

Databases (Scopus, PubMed and Google Scholar) were searched for studies based on a rigorous search strategy.

RESULTS

Twenty-four journal papers were selected, in which inertial sensors were the only wearable system employed for FRA in the wild. Gait was the most-investigated activity; but sitting, standing, lying, transitions and gait events, such as turns and missteps, were also explored. A multitude of free-living fall predictors (FLFPs), e.g., the quantity of daily steps, were extracted from activity bouts and events. FLFPs were further categorized into discrete domains (e.g., pace, complexity) defined by conceptual or data-driven models. Heterogeneity was found within the reviewed studies, which includes variance in: terminology (e.g., quantity vs macro), hyperparameters to define/estimate FLFPs, models and domains, and data processing approaches (e.g., the cut-off thresholds to define an ambulatory bout). These inconsistencies led to different results for similar FLFPs, limiting the ability to interpret and compare the evidence.

CONCLUSION

Free-living FRA is a promising avenue for fall prevention. Achieving a harmonized model is necessary to systematically address the inconsistencies in the field and identify FLFPs with the highest predictive values for falls to eventually address intervention programs and fall prevention.

Will We Do If We Can? Habitual Qualitative and Quantitative Physical Activity in Multi-Morbid, Older Persons with Cognitive Impairment

This study aimed to identify determinants of quantitative dimensions of physical activity (PA; duration, frequency, and intensity) in community-dwelling, multi-morbid, older persons with cognitive impairment (CI). In addition, qualitative and quantitative aspects of habitual PA have been described. Quantitative PA and qualitative gait characteristics while walking straight and while walking turns were documented by a validated, sensor-based activity monitor. Univariate and multiple linear regression analyses were performed to delineate associations of quantitative PA dimensions with qualitative characteristics of gait performance and further potential influencing factors (motor capacity measures, demographic, and health-related parameters). In 94 multi-morbid, older adults (82.3 ± 5.9 years) with CI (Mini-Mental State Examination score: 23.3 ± 2.4), analyses of quantitative and qualitative PA documented highly inactive behavior (89.6% inactivity) and a high incidence of gait deficits, respectively. The multiple regression models (adjusted R² = 0.395–0.679, all p < 0.001) identified specific qualitative gait characteristics as independent determinants for all quantitative PA dimensions, whereas motor capacity was an independent determinant only for the PA dimension duration. Demographic and health-related parameters were not identified as independent determinants. High associations between innovative, qualitative, and established, quantitative PA performances may suggest gait quality as a potential target to increase quantity of PA in multi-morbid, older persons.

Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders

Falls are the leading cause of mortality, morbidity and poor quality of life in older adults with or without neurological conditions. Applying machine learning (ML) models to gait analysis outcomes offers the opportunity to identify individuals at risk of future falls. The aim of this study was to determine the effect of different data pre-processing methods on the performance of ML models to classify neurological patients who have fallen from those who have not for future fall risk assessment. Gait was assessed using wearables in clinic while walking 20 m at a self-selected comfortable pace in 349 (159 fallers, 190 non-fallers) neurological patients. Six different ML models were trained on data pre-processed with three techniques such as standardisation, principal component analysis (PCA) and path signature method. Fallers walked more slowly, with shorter strides and longer stride duration compared to non-fallers. Overall, model accuracy ranged between 48% and 98% with 43-99% sensitivity and 48-98% specificity. A random forest (RF) classifier trained on data pre-processed with the path signature method gave optimal classification accuracy of 98% with 99% sensitivity and 98% specificity. Data pre-processing directly influences the accuracy of ML models for the accurate classification of fallers. Using gait analysis with trained ML models can act as a tool for the proactive assessment of fall risk and support clinical decision-making.

Turning duration and steps predict future falls in poststroke hemiplegic individuals: A preliminary cohort study

Introduction: Turning was reported as one of the activities that most frequently leads to falling among stroke patients. This study investigated whether the duration and steps of a 180° turn while walking can distinguish retrospective fallers from non-fallers and predict future falls in a 1-year period in patients with poststroke hemiplegia. Methods: Thirty stroke patients were recruited. They were instructed to get up from a chair, walk straight 3 m, turn around, and return to seated position to assess the 180° walking-turn task. Turning performance was measured by two inertial sensor units of Physilog. Turn duration and steps were recorded for analysis. The numbers of retrospective and prospective falls were also obtained. Results: No significant difference was observed between retrospective stroke fallers and non-fallers in turn duration and steps. Turn duration and steps were significantly greater in prospective stroke fallers than in non-fallers. The cutoff turn duration of 4 s (area under the curve 0.75, 95% CI: 0.56-0.93, sensitivity 67%, specificity 80%, p =.04) and turn step of 7 steps (area under the curve 0.73, 95% CI: 0.51-0.94, sensitivity 56%, specificity 85%, p =.05) were found to most accurately predict prospective stroke fallers from non-fallers. Conclusions: Turn duration and steps were unable to discriminate between retrospective fallers and non-fallers but could predict prospective falls in patients with stroke. More than 4 s or 7 steps to complete a 180° turn while walking can be a predictor for patients with stroke at an increased risk of falling.

Quantity and quality of gait and turning in people with multiple sclerosis, Parkinson's disease and matched controls during daily living

Clinical trials need to specify which specific gait characteristics to monitor as mobility measures for each neurological disorder. As a first step, this study aimed to investigate a set of measures from daily-life monitoring that best discriminate mobility between people with multiple sclerosis (MS) and age-matched healthy control subjects (MS-Ctl) and between people with Parkinson's disease (PD) and age-matched healthy control subjects (PD-Ctl). Further, we investigated how these discriminative measures relate to the disease severity of MS or PD. We recruited 13 people with MS, 21 MS-Ctl, 29 people with idiopathic PD, and 20 PD-Ctl. Subjects wore 3 inertial sensors on their feet and the lumbar back for a week. The Area Under Curves (AUC) from the receiver operator characteristic (ROC) plot was calculated for each measure to determine the objective measures that best separated the MS and PD groups from their respective control cohorts. Adherence wearing the sensors was similar among groups for 58-66 h of recording (p = 0.14). Quantity of mobility (activity measures, such as a median number of strides per gait bout, AUC = 0.93) best discriminated mobility impairments in MS from MS-Ctl. In contrast, quality of mobility (such as turn angle, AUC = 0.90) best discriminated mobility impairments in PD from PD-Ctl. Mobility measures with AUC > 0.80 were correlated with MS and PD clinical scores of disease severity. Thus, measures characterizing mobility impairments differ for MS versus PD during daily life suggesting that mobility measures for clinical trials and clinical practice need to be specific to each neurological disorder.

Validity of Instrumented 360° Turn Test in Older Adults with Cognitive Impairment

AIMS

To examine concurrent and construct validity of inertial sensor 360°turn measures in relation with motion capture and mobility assessments in cognitively impaired older adults.

METHODS

Data was collected in 31 participants, mean age 85.2 (SD 5.2), during clockwise (CW) and counter clockwise (CCW) 360° turns using (1) APDM body-worn inertial sensors and (2) Qualisys 8-camera laboratory-based motion capture.

RESULTS

Absolute agreement between inertial sensor and motion capture measures was excellent for turn duration and turn peak velocity (ICC = 0.96-0.98). Strong to moderate correlations were present between inertial sensor turn measures and performance on the Timed Up and Go, Short Physical Performance Battery and 90-s Balance Test. ROC curve analysis of CCW 360° turn duration and turn peak velocity distinguished higher risk versus lower risk for mobility disability.

CONCLUSIONS

Inertial sensor 360° turn measures demonstrated concurrent and construct validity in relation to motion capture and mobility assessments.

Digital Biomarkers of Mobility in Parkinson's Disease During Daily Living

BACKGROUND

Identifying digital biomarkers of mobility is important for clinical trials in Parkinson's disease (PD).

OBJECTIVE

To determine which digital outcome measures of mobility discriminate mobility in people with PD from healthy control (HC) subjects over a week of continuous monitoring.

METHODS

We recruited 29 people with PD, and 27 age-matched HC subjects. Subjects were asked to wear three inertial sensors (Opal by APDM) attached to both feet and to the lumbar region, and a subset of subjects also wore two wrist sensors, for a week of continuous monitoring. We derived 43 digital outcome measures of mobility grouped into five domains. An Area Under Curve (AUC) was calculated for each digital outcome measures of mobility, and logistic regression employing a 'best subsets selection strategy' was used to find combinations of measures that discriminated mobility in PD from HC.

RESULTS

Duration of recordings was 66±14 hours in the PD and 59±16 hours in the HC. Out of a total of 43 digital outcome measures of mobility, we found six digital outcome measures of mobility with AUC > 0.80. Turn angle (AUC = 0.89, 95% CI: 0.79-0.97) and swing time variability (AUC = 0.87, 95% CI: 0.75-0.96) were the most discriminative individual measures. Turning measures were most consistently selected via the best subsets strategy to discriminate people with PD from HC, followed by gait variability measures.

CONCLUSION

Clinical studies and clinical practice with digital biomarkers of daily life mobility in PD should include turning and variability measures.

Reactive Postural Responses to Continuous Yaw Perturbations in Healthy Humans: The Effect of Aging

Maintaining balance stability while turning in a quasi-static stance and/or in dynamic motion requires proper recovery mechanisms to manage sudden center-of-mass displacement. Furthermore, falls during turning are among the main concerns of community-dwelling elderly population. This study investigates the effect of aging on reactive postural responses to continuous yaw perturbations on a cohort of 10 young adults (mean age 28 ± 3 years old) and 10 older adults (mean age 61 ± 4 years old). Subjects underwent external continuous yaw perturbations provided by the RotoBit^1D platform. Different conditions of visual feedback (eyes opened and eyes closed) and perturbation intensity, i.e., sinusoidal rotations on the horizontal plane at different frequencies (0.2 Hz and 0.3 Hz), were applied. Kinematics of axial body segments was gathered using three inertial measurement units. In order to measure reactive postural responses, we measured body-absolute and joint absolute rotations, center-of-mass displacement, body sway, and inter-joint coordination. Older adults showed significant reduction in horizontal rotations of body segments and joints, as well as in center-of-mass displacement. Furthermore, older adults manifested a greater variability in reactive postural responses than younger adults. The abnormal reactive postural responses observed in older adults might contribute to the well-known age-related difficulty in dealing with balance control during turning.

IMU, sEMG, or their cross-correlation and temporal similarities: Which signal features detect lateral compensatory balance reactions more accurately?

BACKGROUND AND OBJECTIVE

Falls are the leading cause of fatal and non-fatal injuries among seniors worldwide. While laboratory evidence supports the view that impaired ability to execute compensatory balance responses (CBRs) is linked to an increased risk of falling, existing unsupervised fall risk assessment methods are mainly focused on detecting changes in spatio-temporal gait parameters over time rather than naturally-occurring CBR events. To address the gap in available methods, this paper compares the capability of machine learning-based models trained on the kinematic data from inertial measurement units (IMU) and surface electromyography (sEMG) features to detect lateral CBRs, to ultimately address detection of CBRs in free-living conditions. Moreover, we propose a novel "Hybrid" feature set, which considers cross-correlation and temporal similarities between the normalized kinematic and sEMG signals.

METHODS

Focusing on frontal plane perturbations, a classifier to automatically: 1) detect lateral CBRs during normal gait, and 2) identify type (i.e., crossover, sidestep) using data from three wearable IMUs and 4 sEMG signals from the thigh (i.e., biceps femoris, rectus femoris) and lower leg muscles (i.e., gastrocnemious, tibialis anterior) was developed. In total, 600 trials (including 358 lateral CBRs) from 7 young, healthy adults were analyzed. The effects of feature type (IMU, sEMG, Hybrid) and sensor placement on the random forest-based classifier performance were investigated.

RESULTS

CBR detection (i.e., CBR vs normal gait) accuracies (leave-one-subject-out cross validation) were 83.95% and 99.21% using sEMG-based and IMU-based features, respectively, which dropped to 72.17% and 84.83% for the multiclass identification (i.e., side-step vs cross-over vs normal gait) problem. Findings yielded shank as the best overall location for the multiclass problem, and chest as the most accurate for CBR detection. In general, adding sEMG and Hybrid features to IMUs yielded incremental improvements in CBR detection and type identification (87.03% leave-one-subject-out cross-validation for type identification).

CONCLUSION

The findings of this study demonstrate that IMU-based features are favourable over sEMG and Hybrid features for the task of CBR detection, with incremental value for type identification. Evidence presented suggests that Hybrid features may increase performance for other wearable sensor applications (e.g. activity recognition systems).

Automated Detection of Multidirectional Compensatory Balance Reactions: A Step Towards Tracking Naturally Occurring Near Falls

Falls are the leading cause of fatal and non-fatal injuries among seniors with serious and costly consequences. Laboratory evidence supports the view that impaired ability to execute compensatory balance reactions (CBRs) or near-falls is linked to an increased risk of falling. Therefore, as an alternative to the commonly used fall risk assessment methods examining spatial-temporal parameters of gait, this study focuses on the development of machine learning-based models to detect multidirectional CBRs using wearable inertial measurement units (IMUs). Random forest models were developed based upon the data captured by five wearable IMUs to 1) detect CBRs during normal gait, and 2) identify the type of CBR (eight different classes). A perturbation treadmill (PT) was employed to systematically elicit CBRs (i.e. PT-CBRs) during walking in different directions (e.g slip-like, trip-like, and medio-lateral) and amplitudes (e.g., low-, high-amplitude). We hypothesized that these PT-CBRs could simulate naturally-occurring CBRs (N-CBRs). Proof-of-concept testing in 9 young, healthy adults demonstrated accuracies of 96.60% and 80.64% for the PT-CBR detection and type identification models, respectively. Performance of the detection model was tested against a published dataset (IMUFD) simulating N-CBRs, including the most common types observed in older adults in long-term care facilities, which achieved sensitivity of 100%, but poor specificity. Adding normal gait data from IMUFD for training improved specificity, indicating treadmill walking alone is insufficient exemplar data. Perturbation treadmill combined with overground walking data is a suitable paradigm to collect training datasets of involuntary CBR events. These findings suggest that accurate detection of naturally-occurring CBRs is feasible, and supports further investigation of implementing a wearable sensor system to track naturally-occurring CBRs as a novel means of fall risk assessment.

Associations between Turning Characteristics and Corticospinal Inhibition in Young and Older Adults

The effects of aging are multifaceted including deleterious changes to the structure and function of the nervous system which often results in reduced mobility and quality of life. Turning while walking (dynamic) and in-place (stable) are ubiquitous aspects of mobility and have substantial consequences if performed poorly. Further, turning is thought to require higher cortical control compared to bouts of straight-ahead walking. This study sought to understand how relative amounts of corticospinal inhibition as measured by transcranial magnetic stimulation and the cortical silent period within the primary motor cortices are associated with various turning characteristics in neurotypical young (YA) and older adults (OA). In the current study, OA had reduced peak turn velocity and increased turn duration for both dynamic and stable turns. Further, OA demonstrated significantly reduced corticospinal inhibition within the right motor cortex. Finally, all associations between corticospinal inhibition and turning performance were specific to the right hemisphere, reflecting that those OA who maintained high levels of inhibition performed turning similar to their younger counterparts. These results compliment the right hemisphere model of aging and lateralization specification of cortically regulated temporal measures of dynamic movement. While additional investigations are required, these pilot findings provide an additional understanding as to the neural control of dynamic movements.

Effect of walking surface, late-cueing, physiological characteristics of aging, and gait parameters on turn style preference in healthy, older adults

Turning while walking is a crucial component of locomotion, often performed on irregular surfaces with little planning time. Turns can be difficult for some older adults due to physiological age-related changes. Two different turning strategies have been identified in the literature. During step turns, which are biomechanically stable, the body rotates about the outside limb, while for spin turns, generally performed with closer foot-to-foot distance, the inside limb is the main pivot point. Turning strategy preferences of older adults under challenging conditions remains unclear. The aim of this study was to determine how turning strategy preference in healthy older adults is modulated by surface features, cueing time, physiological characteristics of aging, and gait parameters. Seventeen healthy older adults (71.5 ± 4.2 years) performed 90° turns for two surfaces (flat, uneven) and two cue conditions (pre-planned, late-cue). Gait parameters were identified from kinematic data. Measures of lower-limb strength, balance, and reaction-time were also recorded. Generalized linear (logistic) regression mixed-effects models examined the effect of (1) surface and cuing, (2) physiological characteristics of ageing, and (3) gait parameters on turn strategy preference. Step turns were preferred when the condition was pre-planned (p < 0.001) (model 1) and when the gait parameters of stride regularity and maximum acceleration decreased (p = 0.010 and p = 0.039, respectively) (model 3). Differences in turn strategy selection under dynamic conditions ought to be evaluated in future fall-risk research and rehabilitation utilizing real-world activity monitoring.

Walking Along Curved Trajectories. Changes With Age and Parkinson's Disease. Hints to Rehabilitation

In this review, we briefly recall the fundamental processes allowing us to change locomotion trajectory and keep walking along a curved path and provide a review of contemporary literature on turning in older adults and people with Parkinson's Disease (PD). The first part briefly summarizes the way the body exploits the physical laws to produce a curved walking trajectory. Then, the changes in muscle and brain activation underpinning this task, and the promoting role of proprioception, are briefly considered. Another section is devoted to the gait changes occurring in curved walking and steering with aging. Further, freezing during turning and rehabilitation of curved walking in patients with PD is mentioned in the last part. Obviously, as the research on body steering while walking or turning has boomed in the last 10 years, the relevant critical issues have been tackled and ways to improve this locomotor task proposed. Rationale and evidences for successful training procedures are available, to potentially reduce the risk of falling in both older adults and patients with PD. A better understanding of the pathophysiology of steering, of the subtle but vital interaction between posture, balance, and progression along non-linear trajectories, and of the residual motor learning capacities in these cohorts may provide solid bases for new rehabilitative approaches.

Prominent physical inactivity in acute dementia care: Psychopathology seems to be more important than the dose of sedative medication

INTRODUCTION

To objectively quantify patients' physical activity and analyze the relationships between physical activity levels, psychopathology, and sedative medication in acute hospital dementia care.

MATERIALS AND METHODS

In this cross-sectional study, we assessed the patients' physical activity based on data collection by hybrid motion sensors attached on their lower back. Daily doses of antipsychotics have been converted to olanzapine-equivalents and daily benzodiazepine medication is reported as diazepam-equivalents. We assessed patients' neuropsychiatric symptoms with the Neuropsychiatric Inventory and the Cohen-Mansfield Agitation Inventory.

RESULTS

We analyzed motion sensor data from 64 patients (MMSE M = 18.6). On average, patients were lying for 11.5 hours, sitting/standing sedentary for 10.3 hours, sitting/standing active for 1.0 hours, and walking for 1.2 hours per day. The analysis revealed no correlations between patients' physical activity and antipsychotic or benzodiazepine medication. More severe neuropsychiatric symptoms were associated with a decrease in the patients' physical activity (r = .32, P = .01). In particular, patients with apathy symptoms were less physically active than patients without apathy symptoms.

DISCUSSION

The results reveal that most of the patients in acute dementia care had very low levels of physical activity. Their physical inactivity may be due to the severity of their neuropsychiatric symptoms, especially apathy. Antipsychotic and benzodiazepine medication appeared to have less impact on patients' physical activity. Dementia care should pay more attention to prevent physical inactivity in patients.

Application of Wearable Inertial Sensors and A New Test Battery for Distinguishing Retrospective Fallers from Non-fallers among Community-dwelling Older People

Considering the challenge of population ageing and the substantial health problem among the elderly population from falls, the purpose of this study was to verify whether it is possible to distinguish accurately between older fallers and non-fallers, based on data from wearable inertial sensors collected during a specially designed test battery. A comprehensive but practical test battery using 5 wearable inertial sensors for multifactorial fall risk assessment was designed. This was followed by an experimental study on 196 community-dwelling Korean older women, categorized as fallers (N1 = 82) and non-fallers (N2 = 114) based on prior history of falls. Six machine learning models (logistic regression, naïve bayes, decision tree, random forest, boosted tree and support vector machine) were proposed for faller classification. Results indicated that compared with non-fallers, fallers performed significantly worse on the test battery. In addition, the application of sensor data and support vector machine for faller classification achieved an overall accuracy of 89.4% with 92.7% sensitivity and 84.9% specificity. These findings suggest that wearable inertial sensor based systems show promise for elderly fall risk assessment, which could be implemented in clinical practice to identify "at-risk" individuals reliably to promote proactive fall prevention.

Statistical learning of mobility patterns from long-term monitoring of locomotor behaviour with body-worn sensors

Long term monitoring of locomotor behaviour in humans using body-worn sensors can provide insight into the dynamical structure of locomotion, which can be used for quantitative, predictive and classification analyses in a biomedical context. A frequently used approach to study daily life locomotor behaviour in different population groups involves categorisation of locomotion into various states as a basis for subsequent analyses of differences in locomotor behaviour. In this work, we use such a categorisation to develop two feature sets, namely state probability and transition rates between states, and use supervised classification techniques to demonstrate differences in locomotor behaviour. We use this to study the influence of various states in differentiating between older adults with and without dementia. We further assess the contribution of each state and transition and identify the states most influential in maximising the classification accuracy between the two groups. The methods developed here are general and can be applied to areas dealing with categorical time series.